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ranapratapmajee

Omega Tools MCP

by ranapratapmajee
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Python
Hybrid

🌌 Omega Core Infrastructure Server (Model Context Protocol)

A production-grade, highly decoupled Model Context Protocol (MCP) server that acts as a centralized microservice toolkit for LLM agents (Cline, Cursor, Custom Independent Agents, etc.).

Omega is explicitly architected to enforce a strict Separation of Concerns (SoC) by separating protocol-agnostic backend service engines from the AI presentation layer. It natively runs as a scalable, containerized SSE (Server-Sent Events) network deployment inside Docker on port 8080.

🏗️ Architectural Layout

The project uses a strict layer separation pattern to guarantee that AI semantic descriptions never bleed into raw infrastructure connectivity modules:

src/omega_mcp/
├── config.py         # Standardized configuration schemas, env variable parsing, and automated boot validations
├── logger.py         # Custom telemetry logging engine directing output safely to sys.stderr
├── server.py         # Central ASGI network routing gateway, tool registry, and lifespan state orchestrator
├── core/             # PROTOCOL-AGNOSTIC DATA ENGINES (Pure Python Data Types Only)
│   ├── service_alpha.py # Foundation engine logic handling connection state pools, file systems, or databases
│   └── service_beta.py  # Standalone service driver handling network adapters, utilities, or external APIs
└── tools/            # SYMMETRICAL PRESENTATION LAYERS (Maps Core Logic to Symmetrical XML)
    ├── tool_alpha.py    # Pulls lifecycle state connections from service_alpha and compiles uniform XML records
    └── tool_beta.py     # Invokes stateless service_beta sequences and converts outputs to standard XML responses

🛠️ The Symmetrical Presentation Pattern

To completely eliminate Context Structure Clash—where an LLM's attention heads accidentally favor one tool pattern or output layout over another—all tools added to this repository must convert core internal records into a uniform, identical XML structural layout (<knowledge_source> $\rightarrow$ <record>):

<knowledge_source type="source_type" query="target_query">
  <record id="unique_identifier" score="relevance_weight_if_applicable">
    <specific_fact>Extracted text body content payload goes here...</specific_fact>
    <parent_lineage id="parent_id">Title, Source Reference, or Provenance Metadata Group</parent_lineage>
    <semantic_entities>comma, separated, key, concept, tags</semantic_entities>
  </record>
</knowledge_source>

Related MCP server: MCP Server with OpenAI Integration

🧱 System Architecture & Container Data Flow

Omega runs entirely within an isolated Docker container communicating via Server-Sent Events (SSE). This transforms it into a global network tool mesh accessible by local editors and remote agents simultaneously.

  ┌──────────────────────┐      ┌───────────────────────────────┐
  │   VS Code Client     │      │   External Python Agent       │
  │   (Cline / Cursor)   │      │     (Google Gen AI SDK)       │
  └──────────┬───────────┘      └───────────────┬───────────────┘
             │                                  │
             ▼ [HTTP/SSE Network Connection]    ▼ [HTTP POST JSON-RPC]
  ┌─────────────────────────────────────────────────────────────┐
  │                 OMEGA CORE INFRASTRUCTURE SERVER            │
  │                                                             │
  │  ┌───────────┐      ┌─────────────────────────────────────┐ │
  │  │ server.py │ ───> │ tools/tool_alpha.py                 │ │
  │  └─────┬─────┘      │ tools/tool_beta.py                  │ │
  │        │            │ (Symmetrical XML Payload Compilers) │ │
  │        │            └───────────────┬─────────────────────┘ │
  │        ▼ [Lifespan Injection]       │                       │
  │  ┌───────────────────┐              │                       │
  │  │  core/service_*   │◄─────────────┘                       │
  │  └─────────┬─────────┘                                      │
  └────────────┼────────────────────────────────────────────────┘
               │
               ▼ [Network Socket Drivers / API Protocols]
┌───────────────────────────────────────────────────────────────┐
│ Target Infrastructure Layers (Databases, Cloud APIs, Systems) │
└───────────────────────────────────────────────────────────────┘

🐳 Production Deployment via Docker

The project defaults to application-workspace execution mode. Dependencies are managed and synchronized cleanly via uv straight inside the container layer.

1. Build the Docker Image Locally

docker build -t omega-mcp .

2. Configure Your Cluster Engine Layout (docker-compose.yaml)

To mount the server into your architecture stack, map external host port 8080 to the internal container web gateway port 8000:

services:
  omega-mcp:
    image: omega-mcp:latest
    container_name: nexus-tools-mcp
    ports:
      - "8080:8000" # Host Port 8080 -> Container Port 8000
    environment:
      - ENV=production
      - MCP_TRANSPORT=sse
      - MCP_HOST=0.0.0.0
      - MCP_PORT=8000
      
      # Core Service Parameter Allocations
      - SERVICE_ALPHA_URL=http://your-infrastructure-target:port
      - SERVICE_BETA_CREDENTIAL=your_secure_access_token
    restart: unless-stopped

Boot up the background microservice network container:

docker compose up -d

🔌 Connecting to AI Clients

1. IDE Client Setup (Cline / VS Code Extension Configuration)

Because the server runs via Docker SSE, your IDE does not need to handle local Python virtual environments or sub-processes. Point your configuration directly to the live SSE network route:

{
  "mcpServers": {
    "omega-tools-docker": {
      "url": "http://localhost:8080/sse"
    }
  }
}

2. Consuming Tools from Another Project (External Python Agent)

To consume these microservices inside a separate Python service or custom LLM agent framework (e.g., Google Gen AI SDK), call the explicit tool execution paths via HTTP POST requests:

import httpx
from google import genai

ai_client = genai.Client()

def call_mcp_custom_tool(query: str) -> str:
    """Consumes the containerized MCP tool via standard network transport."""
    # FastMCP exposes active tool executions via /tools/{mcp_registered_tool_name}/call
    CONTAINER_URL = "http://localhost:8080/tools/registered_tool_name/call"
    
    try:
        response = httpx.post(CONTAINER_URL, json={"arguments": {"query": query}}, timeout=30.0)
        response.raise_for_status()
        
        # Extract content payload string directly out of standard JSON-RPC schema
        return response.json()["content"][0]["text"]
    except Exception as e:
        return f"<knowledge_source type='custom_tool' status='ERROR' details='{str(e)}'/>"

# Bind directly as a native function tool to your independent agent loop
research_agent = Agent(
    name="ResearchAgent",
    model=ai_client,
    tools=[call_mcp_custom_tool],
    instruction="Execute objective analysis using the provided infrastructure tool endpoint."
)

📈 Scaling Up: Adding New Tools Cleanly

Omega is engineered to expand fluidly. When adding new capabilities, respect the core architectural boundaries by isolating processing routines from interface parsing.

🔹 Step 1: Write the Core Domain Logic

Create a protocol-agnostic service module inside src/omega_mcp/core/ to process your raw metrics or lookups using pure Python types:

# filepath: src/omega_mcp/core/analytics.py
class AnalyticsService:
    async def fetch_metrics(self, target_id: str) -> dict:
        # Pure database lookups, computational algorithms, or external API fetches
        return {"id": target_id, "status": "active", "metrics": [88, 92, 95]}

🔹 Step 2: Create the Tool Presentation Layer

Create a corresponding interface script inside src/omega_mcp/tools/ to consume your core engine and map its outputs to the Symmetrical XML Schema:

# filepath: src/omega_mcp/tools/analytics_search.py
from omega_mcp.core.analytics import AnalyticsService

_service = AnalyticsService()

async def execute_analytics_tool(target_id: str) -> str:
    data = await _service.fetch_metrics(target_id)
    
    # Compile the uniform symmetrical XML layout for the LLM context window
    return (
        f"<knowledge_source type='custom_analytics' query='{target_id}'>\n"
        f"  <record id='{data['id']}'>\n"
        f"    <specific_fact>Status is {data['status']} with calculated scores.</specific_fact>\n"
        f"    <parent_lineage id='cluster_node'>Internal Operational Cluster</parent_lineage>\n"
        f"    <semantic_entities>{', '.join(map(str, data['metrics']))}</semantic_entities>\n"
        f"  </record>\n"
        f"</knowledge_source>"
    )

🔹 Step 3: Register the Declarative Route to the Gateway

Open src/omega_mcp/server.py and bind your new interface function to the central FastMCP instance using the standard decorators:

# filepath: src/omega_mcp/server.py
from omega_mcp.tools.analytics_search import execute_analytics_tool

@mcp.tool(name="get_custom_metrics", description="Queries internal processing performance metrics.")
async def tool_custom_metrics(target_id: str) -> str:
    return await execute_analytics_tool(target_id)

🔹 Step 4: Recycle Your Container Stack

Rebuild your Docker container image layers and refresh the cluster setup:

docker build -t omega-mcp .
docker compose up -d --force-recreate omega-mcp

🔒 Logging & Architecture Guardrails

  • Telemetry Isolation: High-frequency framework logs from downstream database connection components are suppressed to WARNING level inside server.py during initialization loops to prevent network telemetry flooding.

  • Stream Defenses: All logging implementations channel lines to sys.stderr safely, keeping container log structures intact while freeing up main transport execution lines.

  • Decoupled Design: Files created under core/ have zero dependencies on the mcp library package, ensuring that your core infrastructure operations remain clean, reusable, and completely independent of the endpoint framework.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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